The doctoral research abstracts. Vol:8 2015 / Institute of Graduate Studies, UiTM

Foreword: THIRTY FIRST October 2015 marks the celebration of 47 PhD doctorates receiving their scroll during UiTM 83rd Convocation Ceremony. This date is significant to UiTM since it is an official indication of 47 more scholarly contributions to the world of knowledge and innovation through the novelty of their research. To date UiTM has contributed 471 producers of knowledge through their doctoral research ranging from the field of Science and Technology, Business and Administration, and Social Science and Humanities. This Doctoral Abstracts epitomizes knowledge par excellence and a form of tribute to the 47 doctorates whose achievement we proudly celebrate. To the graduands, your success in achieving the highest academic qualification has demonstrated that you have indeed engineered your destiny well. The action of registering for a PhD program was not by chance but by choice. It was a choice made to realise your self-actualization level that is the highest level in Maslow’s Hierarchy of Needs, while at the same time unleashing your potential in the scholarly research. Do not forget that life is a treasure and that its contents continue to be a mystery, thus, your journey of discovery through research has not come to an end but rather, is just the beginning. Enjoy life through your continuous discovery of knowledge, and spearhead innovation while you are at it. Make your alma mater proud through this continuous discovery as alumni of UiTM. As you soar upwards in your career, my advice will be to continuously be humble and ‘plant’ your feet firmly on the ground. Congratulations once again and may you carry UiTM as ‘Sentiasa di Hatiku’. Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar, FASc, PEng Vice Chancellor Universiti Teknologi MAR

APPLICATION OF ARTIFICIAL NEURAL NETWORK TECHNIQUES FOR DESIGN OF MODULAR MINICELL CONFIGURATIONS

Artificial neural networks, so far, have not been used for designing modular cells. Therefore, Self-organizing neural network (SONN) is used in the present research to design minicell-based manufacturing system. Two previously developed methods were studied and implemented using SONN model. Results obtained are compared with previous results to analyze the effectiveness of SONN in designing minicells. A new method is then developed with the objective to design minicells more effectively and efficiently. Results of all three methods are compared using machine-count and materialhandling as performance measuring criteria to find out the best metho

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Applicability and Interpretability of Logical Analysis of Data in Condition Based Maintenance

Résumé Cette thèse étudie l’applicabilité et l’adaptabilité d’une approche d’exploration de données basée sur l’intelligence artificielle proposée dans [Hammer, 1986] et appelée analyse logique de données (LAD) aux applications diagnostiques dans le domaine de la maintenance conditionnelle CBM). La plupart des technologies utilisées à ce jour pour la prise de décision dans la maintenance conditionnelle ont tendance à automatiser le processus de diagnostic, sans offrir aucune connaissance ajoutée qui pourrait être utile à l’opération de maintenance et au personnel de maintenance. Par comparaison à d’autres techniques de prise de décision dans le domaine de la CBM, la LAD possède deux avantages majeurs : (1) il s’agit d’une approche non statistique, donc les données n’ont pas à satisfaire des suppositions statistiques et (2) elle génère des formes interprétables qui pourraient aider à résoudre les problèmes de maintenance. Une étude sur l’application de la LAD dans la maintenance conditionnelle est présentée dans cette recherche dont l’objectif est (1) d’étudier l’applicabilité de la LAD dans des situations différentes qui nécessitent des considérations particulières concernant les types de données d’entrée et les décisions de maintenance, (2) d’adapter la méthode LAD aux exigences particulières qui se posent à partir de ces applications et (3) d’améliorer la méthodologie LAD afin d’augmenter l’exactitude de diagnostic et d’interprétation de résultats. Les aspects innovants de la recherche présentés dans cette thèse sont (1) l’application de la LAD dans la CBM pour la première fois dans des applications qui bénéficient des propriétés uniques de cette technologie et (2) les modifications innovatrices de la méthodologie de la LAD, en particulier dans le domaine de la génération des formes, afin d’améliorer ses performances dans le cadre de la CBM et dans le domaine de classification multiclasses. La recherche menée dans cette thèse a suivi une approche évolutive afin d’atteindre les objectifs énoncés ci-dessus. La LAD a été utilisée et adaptée à trois applications : (1) la détection des composants malveillants (Rogue) dans l’inventaire de pièces de rechange réparables d’une compagnie aérienne commerciale, (2) la détection et l’identification des défauts dans les transformateurs de puissance en utilisant la DGA et (3) la détection des défauts dans les rotors en utilisant des signaux de vibration. Cette recherche conclut que la LAD est une approche de prise de décision prometteuse qui ajoute d’importants avantages à la mise en oeuvre de la CBM dans l’industrie.----------Abstract This thesis studies the applicability and adaptability of a data mining artificial intelligence approach called Logical Analysis of Data (LAD) to diagnostic applications in Condition Based Maintenance (CBM). Most of the technologies used so far for decision support in CBM tend to automate the diagnostic process without offering any added knowledge that could be helpful to the maintenance operation and maintenance personnel. LAD possesses two key advantages over other decision making technologies used in CBM: (1) it is a non-statistical approach; as such no statistical assumptions are required for the input data, and (2) it generates interpretable patterns that could help solve maintenance problems. A study on the implementation of LAD in CBM is presented in this research whose objective are to study the applicability of LAD in different CBM situations requiring special considerations regarding the types of input data and maintenance decisions, adapt the LAD methodology to the particular requirements that arise from these applications, and improve the LAD methodology in line with the above two objectives in order to increase diagnosis accuracy and result interpretability. The novelty of the research presented in this thesis is (1) the application of LAD to CBM for the first time in applications that stand to benefit from the advantages that this technology provides; and (2) the innovative modifications to LAD methodology, particularly in the area of pattern generation, in order to improve its performance within the context of CBM. The research conducted in this thesis followed an evolutionary approach in order to achieve the objectives stated in the Introduction. The research applied LAD in three applications: (1) the detection of Rogue components within the spare part inventory of reparable components in a commercial airline company, (2) the detection and identification of faults in power transformers using DGA, and (3) the detection of faults in rotor bearings using vibration signals. This research concludes that LAD is a promising decision making approach that adds important benefits to the implementation of CBM in the industry

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC